Image processing apparatus

ABSTRACT

The invention concerns an image-processing apparatus, in which a high-frequency component signal of an original image-signal, representing a plurality of pixels, is added to either the original image-signal or a lowest frequency image-signal of the original image-signal, in order to generate a processed image-signal. The image-processing apparatus includes: a conversion-processing section to apply a conversion-processing to unsharp image-signals, generated from the original image-signal in respect to a plurality of frequency bands, so as to generate converted unsharp image-signals; a differential processing section to generate differential image-signals obtained from differences between the unsharp image-signals and the converted unsharp image-signals; and an addition-processing section to totally add the differential image-signals to generate the high-frequency component signal of the original image-signal.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a Continuation of U.S. patent application Ser. No. 10/024,045filed Dec. 17, 2001, which, in turn, claimed the priority of JapanesePatent Application Nos. 385405/2000 filed Dec. 19, 2000, and 385406/2000filed Dec. 19, 2000, the priority of all three Applications is herebyclaimed and all three Applications are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

This invention relates to an image processing apparatus, or moreprecisely, an image processing apparatus using a multiple resolutionprocessing.

The processing section of a radiation image uses a method that addsconversion processing to an original image signal. FIG. 20 illustratesan explanation of a conventional frequency emphasizing-processing.Processed image signals are created by creating unsharp image signal 2from original image signal 1, generating differential image signal 3 bysubtracting said unsharp image signal 2 from original image signal 1,and adding the result of multiplication between coefficient β and saiddifferential image signal 3 to original image signal 1 as a correctionsignal.

FIG. 21 illustrates an explanation of a conventional dynamic rangecompression processing. Identical codes are assigned to the itemsidentical in FIG. 20. In this case, differential image signal 3 isgenerated by creating unsharp image signal 2 from original image signal1 and subtracting unsharp image signal 2 from original image signal 1.At the same time, compensation image signal 5 is obtained by addingdensity-compensating conversion 4 to unsharp image signal 2. A processedimage signal is obtained by adding differential image signal 3 to saidcompensation-image signal 5 that was obtained. Recently, a method forobtaining sharper processed image signals has been developed byimproving said image processing method.

As one of the techniques, a multiple resolution method is available.Image processing using said multiple resolution method obtains imagesignals by decomposing an original image signal into image signals ofmultiple frequency bands, adding specified image processing, andrestoring said image signals. Image processing using said multipleresolution method is introduced in (Digital Image Processing:Springer-Verlag 1991). Said document does not describe the processingimplemented by applying conversion processing to a unsharp image signalor a differential image signal that is decomposed. A pyramid algorithmis available as one of the algorithms for high speed image processingusing said multiple resolution method.

Various methods have been proposed as image processing techniques usingsaid pyramid algorithm. However, filters of mask processing used in saidtechniques are always in a fixed shape (for instance, JapaneseApplication Patent Laid-Open Publication No. Hei 10-75395 report).Therefore, frequency characteristics of decomposed image signals areidentical and consequently are not suitable for more delicate frequencyoperations.

SUMMARY OF THE INVENTION

To overcome the abovementioned drawbacks in conventionalimage-processing apparatus, it is an object of the present invention toprovide an image-processing apparatus that enables more delicatefrequency characteristic operations.

It is another object of the present invention to provide animage-processing apparatus that suppresses deterioration of graininesscaused by frequency processing and obtain sufficient emphasis.

Accordingly, to overcome the cited shortcomings, the abovementionedobjects of the present invention can be attained by image-processingapparatus described as follow.

-   (1) An image-processing apparatus, in which a high-frequency    component signal of an original image-signal, representing a    plurality of pixels, is added to either the original image-signal or    a lowest frequency image-signal of the original image-signal, in    order to generate a processed image-signal, comprising: a    conversion-processing section to apply a conversion-processing to    unsharp image-signals, generated from the original image-signal in    respect to a plurality of frequency bands, so as to generate    converted unsharp image-signals; a differential processing section    to generate differential image-signals obtained from differences    between the unsharp image-signals and the converted unsharp    image-signals; and an addition-processing section to totally add the    differential image-signals to generate the high-frequency component    signal of the original image-signal.-   (2) The image-processing apparatus of item 1, wherein the    differential image-signals derive from either differences between    the unsharp image-signals in an adjacent pair of the frequency-bands    or differences between the original image-signal and the converted    unsharp image-signals.-   (3) The image-processing apparatus of item 1, wherein the    conversion-processing is to convert pixel values of the unsharp    image-signals, based on a non-linear transform.-   (4) The image-processing apparatus of item 1, wherein the    conversion-processing is determined by the original image-signal or    the unsharp image-signals in the plurality of frequency-bands.-   (5) The image-processing apparatus of item 1, wherein the    conversion-processing is determined by the original image-signal or    the unsharp image-signals in adjacent pairs of frequency-bands.-   (6) The image-processing apparatus of item 1, wherein the    conversion-processing varies depending on either one of pixel value    of the unsharp image-signals employed for generating the    differential image-signals or pixel values of the original    image-signal.-   (7) The image-processing apparatus of item 1, wherein the    conversion-processing varies depending on the unsharp image-signals.-   (8) The image-processing apparatus of item 1, wherein the    conversion-processing is a suppression-processing for suppressing an    averaging-processing for averaging image-signals.-   (9) The image-processing apparatus of item 1, wherein the    conversion-processing varies depending on pixel values of the    unsharp image-signals to be processed by the conversion-processing.-   (10) The image-processing apparatus of item 1, wherein the    conversion-processing varies depending on pixel values of a unsharp    image-signal at a lowest frequency-band.-   (11) The image-processing apparatus of item 1, wherein the    conversion-processing varies depending on pixel values of the    original image-signal.-   (12) The image-processing apparatus of item 8, wherein the lower a    frequency-band in which the unsharp image-signals reside is, the    greater a degree of suppressing the averaging-action for averaging    the image-signals in the suppression-processing is.-   (13) The image-processing apparatus of item 8, wherein the higher a    frequency-band in which the unsharp image-signals reside is, the    stronger a power of suppressing the averaging-action for averaging    the image-signals in the suppression-processing is.-   (14) An image-processing apparatus, in which a compensation-signal    generated from a low-frequency component signal of an original    image-signal, representing a plurality of pixels, is added to either    the original image-signal or a lowest frequency image-signal of the    original image-signal, in order to generate a processed    image-signal, comprising: a conversion-processing section to apply a    conversion-processing to unsharp image-signals, generated from the    original image-signal in respect to a plurality of frequency bands,    so as to generate converted unsharp image-signals; a differential    processing section to generate differential image-signals obtained    from differences between the unsharp image-signals and the converted    unsharp image-signals; and a compensation-signal calculating section    to totally add the differential image-signals so as to generate a    high-frequency component signal, and to calculate the    compensation-signal by subtracting the low-frequency component    signal form a converted low-frequency component signal, which is    derived from a difference between the high-frequency component    signal and the original image-signal.-   (15) The image-processing apparatus of item 14, wherein the    differential image-signals are derived from either differences    between the unsharp image-signals in adjacent pairs of the    frequency-bands or differences between the original image-signal and    the converted unsharp image-signals.-   (16) The image-processing apparatus of item 14, wherein the    conversion-processing is to convert pixel values of the unsharp    image-signals, based on a non-linear transform.-   (17) The image-processing apparatus of item 14, wherein the    conversion-processing is determined by the original image-signal or    the unsharp image-signals in the plurality of frequency-bands.-   (18) The image-processing apparatus of item 14, wherein the    conversion-processing is determined by the original image-signal or    the unsharp image-signals in an adjacent pair of frequency-bands.-   (19) The image-processing apparatus of item 14, wherein the    conversion-processing varies depending on either one of pixel value    of the unsharp image-signals employed for generating the    differential image-signals or pixel values of the original    image-signal.-   (20) The image-processing apparatus of item 14, wherein the    conversion-processing varies depending on the unsharp image-signals.-   (21) The image-processing apparatus of item 14, wherein the    conversion-processing is a suppression-processing for suppressing an    averaging-processing for averaging image-signals.-   (22) The image-processing apparatus of item 14, wherein the    conversion-processing varies depending on pixel values of the    unsharp image-signals to be processed by the conversion-processing.-   (23) The image-processing apparatus of item 14, wherein the    conversion-processing varies depending on pixel values of a unsharp    image-signal at a lowest frequency-band.-   (24) The image-processing apparatus of item 14, wherein the    conversion-processing varies depending on pixel values of the    original image-signal.-   (25) The image-processing apparatus of item 21, wherein the lower a    frequency-band in which the unsharp image-signals reside is, the    greater a degree of suppressing the averaging-processing for    averaging the image-signals in the suppression-processing is.-   (26) The image-processing apparatus of item 21, wherein the higher a    frequency-band in which the unsharp image-signals reside is, the    stronger a power of suppressing the averaging-action for averaging    the image-signals in the suppression-processing is.-   (27) An image-processing apparatus, comprising: an unsharp    image-signal generating section to generate unsharp image-signals    from an original image-signal in respect to a plurality of    frequency-bands; a differential processing section to generate    differential image-signals from differences between the original    image-signal and the unsharp image-signals, and to apply a    conversion-processing to the differential image-signals so as to    generate converted differential image-signals; and an addition    processing section to add the converted differential image-signals    to the original image-signal or a lowest frequency image-signal to    generate a processed image-signal; wherein the conversion-processing    varies depending on pixel values of the unsharp image-signals.-   (28) The image-processing apparatus of item 27, further comprising:    a compensation-signal calculating section to generate a    compensation-signal which is derived from a low-frequency component    signal obtained by subtracting a total sum of the converted    differential image-signals from the original image-signal; wherein    the addition processing section adds the compensation-signal,    instead of the converted differential image-signals, to the original    image-signal or the lowest frequency image-signal to generate the    processed image-signal.-   (29) The image-processing apparatus of item 28, wherein the    differential image-signals derive from either differences between    the unsharp image-signals in adjacent pairs of the frequency-bands    or differences between the original image-signal and the unsharp    image-signals.-   (30) The image-processing apparatus of item 28, wherein the    differential image-signals on which the conversion-processing    depends are either anyone of image-signals utilized for obtaining    the differential image-signals or both of them.-   (31) The image-processing apparatus of item 28, wherein the    conversion-processing applied to the differential image-signals    varies depending on the differential image-signals.-   (32) The image-processing apparatus of item 28, wherein the    conversion-processing applied to the differential image-signals is a    suppression-processing for suppressing an absolute pixel value at    least at a part of image-signals.-   (33) The image-processing apparatus of item 32, wherein the lower a    frequency-band in which the differential image-signals reside is,    the stronger a power of suppressing the absolute pixel value of the    image-signals in the suppression-processing is.-   (34) The image-processing apparatus of item 32, wherein the higher a    frequency-band in which the differential image-signals reside is,    the stronger a power of suppressing the absolute pixel value of the    image-signals in the suppression-processing is.-   (35) The image-processing apparatus of item 28, wherein a    conversion-function is determined by designating a frequency    characteristic, so as to realize a given frequency characteristic,    and processing are conducted on the basis of the    conversion-function.-   (36) The image-processing apparatus of item 35, wherein the    frequency characteristic can be changed depending on density.-   (37) The image-processing apparatus of item 35, wherein the    frequency characteristic can be changed depending on density of    either the original image-signal or the unsharp image-signals for    every differential image-signal.-   (38) The image-processing apparatus of item 35, wherein sets of    parameters for processing the frequency characteristic are provided    in the image-processing apparatus, a kind of processing can be    designated by selecting one set out of the sets of parameters.-   (39) An image-processing apparatus, comprising: a filter-processing    section to apply a mask-processing to an original image-signal,    representing a plurality of pixels, with a mask so as to generate    filtered original image-signals; an unsharp image-signal generating    section to generate unsharp image-signals from the filtered original    image-signals; a differential processing section to generate    differential image-signals from differences between the original    image-signal and the unsharp image-signals, or from differences    between the unsharp image-signals themselves; and an addition    processing section to add the differential image-signals to the    original image-signal or a lowest frequency image-signal with    respect to the original image-signal in order to generate a    processed image-signal; wherein a frequency characteristic of the    processed image-signal can be varied by changing a frequency    characteristic of the mask employed for the mask-processing.-   (40) The image-processing apparatus of item 39, further comprising:    a compensation-signal calculating section to generate a    compensation-signal which is derived from a low-frequency component    signal obtained by subtracting a total sum of the differential    image-signals from the original image-signal; wherein the addition    processing section adds the compensation-signal, instead of the    differential image-signals, to the original image-signal or the    lowest frequency image-signal to generate the processed    image-signal.-   (41) The image-processing apparatus of item 40, wherein the    mask-processing is repetitions of filtering-processing with a    specific filter.-   (42) The image-processing apparatus of item 41, wherein the mask    employed for the repetitions of filter-processing is a simple    average.-   (43) The image-processing apparatus of item 41, wherein the mask    employed for the repetitions of filter-processing is a simple    average of 2 pixels×2 pixels.-   (44) The image-processing apparatus of item 40, wherein a number of    the repetitions of filter-processing designates the frequency    characteristic of the processed image-signal.-   (45) The image-processing apparatus of item 40, wherein the    frequency characteristic of the processed image-signal is specified    by designating weight of the mask with variance values of a normal    distribution, and a number of the repetitions of filter-processing,    which is approximate to the variance values of the normal    distribution, is calculated to process image-signals.-   (46) The image-processing apparatus of item 40, wherein the    mask-processing varies depending on the unsharp image-signals.-   (47) The image-processing apparatus of item 40, wherein the    mask-processing varies depending on the original image-signal.-   (48) The image-processing apparatus of item 40, wherein the    mask-processing varies depending on a frequency characteristic of    the original image-signal.-   (49) An image-processing apparatus, comprising: an unsharp    image-signal generating section that employs a pyramid algorithm to    generate a plurality of unsharp image-signals, resolutions of which    are different relative to each other, from a original image-signal    representing a plurality of pixels; a differential processing    section to generate differential image-signals from differences    between the original image-signal and the unsharp image-signals, or    from differences between the unsharp image-signals themselves; and    an addition processing section to add the differential image-signals    to the original image-signal or a lowest frequency image-signal with    respect to the original image-signal in order to generate a    processed image-signal; wherein a frequency characteristic of the    processed image-signal can be varied by changing an    interpolation-processing method for adding or subtracting the    unsharp image-signals.-   (50) The image-processing apparatus of item 49, further comprising:    a compensation-signal calculating section to generate a    compensation-signal which is derived from a low-frequency component    signal obtained by subtracting a total sum of the differential    image-signals from the original image-signal; wherein the addition    processing section adds the compensation-signal, instead of the    differential image-signals, to the original image-signal or the    lowest frequency image-signal to generate the processed    image-signal.-   (51) The image-processing apparatus of item 50, wherein the    interpolation-processing is repetitions of filter-processing with a    specific filter.-   (52) The image-processing apparatus of item 51, wherein a mask    employed for the repetitions of filter-processing is a simple    average.-   (53) The image-processing apparatus of item 51, wherein a mask    employed for the repetitions of filter-processing is a simple    average of 2 pixels×2 pixels.-   (54) The image-processing apparatus of item 50, wherein a number of    the repetitions of filter-processing designates the frequency    characteristic of the processed image-signal-   (55) The image-processing apparatus of item 50, wherein the    interpolation-processing is performed on the basis of a sampling    function of the original image-signal.-   (56) The image-processing apparatus of item 50, wherein the    interpolation-processing is a linear-interpolation processing.-   (57) The image-processing apparatus of item 50, wherein the    interpolation-processing is a spline-interpolation processing.-   (58) The image-processing apparatus of item 50, wherein the    interpolation-processing varies depending on a frequency band of a    interpolated image-signal.-   (59) The image-processing apparatus of item 50, wherein the    interpolation-processing varies depending on the original    image-signal.-   (60) The image-processing apparatus of item 50, wherein the    interpolation-processing varies depending on a frequency    characteristic of the original image-signal.-   (61) An image-processing apparatus, comprising: an unsharp    image-signal generating section that employs a pyramid algorithm to    generate a plurality of unsharp image-signals, resolutions of which    are different relative to each other, from a original image-signal    representing a plurality of pixels; a differential processing    section to generate differential image-signals from differences    between the original image-signal and the unsharp image-signals, or    from differences between the unsharp image-signals themselves; and    an addition processing section to add the differential image-signals    to the original image-signal or a lowest frequency image-signal with    respect to the original image-signal in order to generate a    processed image-signal; wherein a mask-processing is employed for    generating the unsharp image-signals in a process of the pyramid    algorithm, and a reduction rate of the unsharp image signals, caused    by a down sampling processing, varies depending on a frequency    characteristic of a mask.-   (62) The image-processing apparatus of item 61, further comprising:    a compensation-signal calculating section to generate a    compensation-signal which is derived from a low-frequency component    signal obtained by subtracting a total sum of the differential    image-signals from the original image-signal; wherein the addition    processing section adds the compensation-signal, instead of the    differential image-signals, to the original image-signal or the    lowest frequency image-signal to generate the processed    image-signal.-   (63) The image-processing apparatus of item 62, wherein the    mask-processing is repetitions of filter-processing with a specific    filter.-   (64) The image-processing apparatus of item 62, wherein the mask    employed for the repetitions of filter-processing is a simple    average.-   (65) The image-processing apparatus of item 62, wherein the mask    employed for the repetitions of filter-processing is a simple    average of 2 pixels×2 pixels.-   (66) The image-processing apparatus of item 62, wherein the    mask-processing varies depending on the unsharp image-signals.-   (67) The image-processing apparatus of item 62, wherein the    mask-processing varies depending on the original image-signal.-   (68) The image-processing apparatus of item 62, wherein the    mask-processing varies depending on a frequency characteristic of an    original image-signal.-   (69) The image-processing apparatus of item 62, wherein a variation    of the frequency characteristic of the mask or a change of an    interpolation-processing is determined by designating a frequency    characteristic.-   (70) The image-processing apparatus of item 69, wherein the    designated frequency characteristic can be changed depending on a    density of the original image-signal or the unsharp image-signals.-   (71) The image-processing apparatus of item 69, wherein the    designated frequency characteristic can be changed depending on a    density of the original image-signal or the unsharp image-signals    for each of the unsharp image-signals and the differential    image-signals.-   (72) The image-processing apparatus of item 39, wherein sets of    parameters for processing the frequency characteristic are provided    in the image-processing apparatus, a kind of processing can be    designated by selecting one set out of the sets of parameters.-   (73) An image-processing apparatus, comprising: an unsharp    image-signal generating section to generate a plurality of unsharp    image-signals from a original image-signal, representing a plurality    of pixels; a differential processing section to generate    differential image-signals from the unsharp image-signals or the    original image-signal; and an addition processing section to add the    differential image-signals to the original image-signal or a lowest    frequency image-signal with respect to the original image-signal in    order to generate a processed image-signal; wherein repetitions of    filter-processing with a specific filter are conducted for    generating the unsharp image-signals.-   (74) The image-processing apparatus of item 73, further comprising:    a compensation-signal calculating section to generate a    compensation-signal which is derived from a low-frequency component    signal obtained by subtracting a total sum of the differential    image-signals from the original image-signal; wherein the addition    processing section adds the compensation-signal, instead of the    differential image-signals, to the original image-signal or the    lowest frequency image-signal to generate the processed    image-signal.-   (75) The image-processing apparatus of item 73, wherein a mask    employed for the repetitions of filter-processing is a simple    average.-   (76) The image-processing apparatus of item 73, wherein a mask    employed for the repetitions of filter-processing is a simple    average of 2 pixels×2 pixels.-   (77) The image-processing apparatus of item 73, wherein a    mask-processing varies depending on the unsharp image-signals.-   (78) The image-processing apparatus of item 73, wherein a    mask-processing varies depending on the original image-signal.-   (79) The image-processing apparatus of item 73, wherein a    mask-processing varies depending on a frequency characteristic of    the original image-signal.-   (80) The image-processing apparatus of item 76, wherein a number of    repetitions of the single average of 2 pixels×2 pixels is not less    than 16.-   (81) The image-processing apparatus of item 76, wherein a number of    repetitions of the single average of 2 pixels×2 pixels is not less    than 8.

Further, to overcome the abovementioned problems, other image-processingapparatus, embodied in the present invention, will be described asfollow:

-   (82) The image processing apparatus that obtains a processed image    signal by adding a high-frequency component signal of an original    image signal for said original image signal comprising multiple    pixels to said original image signal or a lowest frequency image    signal said original signal, wherein said high-frequency component    signal is obtained that by adding a differential image signal    obtained by applying conversion processing to unsharp image signals    of multiple frequency bands that are generated from said original    image signal and adding a difference between said unsharp image    signal and said converted image signal.

By applying this configuration, a differential image signal that isadded to an original image signal is adjusted by converting an unsharpimage signal and, consequently, a processing image signal can be createdwith controlling noise and artifacts together with an edge emphasis.

-   (83) The image processing apparatus, wherein said differential image    signals derive from either differences between said unsharp image    signals in adjacent pairs of frequency bands or differences between    said original image signal and said converted unsharp image signals.

By applying this configuration, frequency band overlapping sections ofdifferential image signals are reduced by taking differences betweenadjacent pairs of unsharp image signals and by applying conversionprocessing to said unsharp image signals, operation in band units isenabled.

-   (84) The image processing apparatus, wherein said conversion    processing applied to said unsharp image signals of said multiple    frequency bands converts pixel values of said original image signals    of said unsharp image signals based on non-linear conversion.

By applying this configuration, edge emphasis and control of noise andartifacts are enabled by performing non-linear conversion.

-   (85) The image processing apparatus, wherein conversion processing    that is applied to unsharp image signals of said multiple frequency    bands is determined by said original image signal or said unsharp    image signals of said multiple frequency band.

By applying this configuration, processing depending on an unsharptendency of image signals can be performed, and consequently, effectiveedge emphasis and noise and artifacts control are enabled.

-   (86) The image processing apparatus, wherein said conversion    processing applied to said unsharp image signals of said multiple    frequency bands is determined by said unsharp image signals in    adjacent pairs of frequency bands or said original image signal.

By applying this configuration, processing depending on an unsharptendency of image signals can be performed, and consequently, effectiveedge emphasis and noise and artifacts control are enabled.

-   (87) The image processing apparatus, wherein said conversion    processing that is applied to said unsharp image signals of said    multiple frequency bands varies according to a pixel value of either    one of said unsharp image signals or said original unsharp image    signal that is used for generating said differential image signals.

By applying this configuration, processing depending on pixels prior tounsharp processing can be performed, enabling conversion with moreconsideration to high frequency component signals, and consequently,more effective edge emphasis and control of artifacts and noise areenabled.

-   (88) The image processing apparatus, wherein said conversion    processing that is applied to said unsharp image signals of said    multiple frequency bands vary according to said unsharp image    signals.

By applying this configuration, adjustments depending on frequency bandscan be made and more effective edge emphasis and control of noise andartifacts are enabled.

-   (89) The image processing apparatus, wherein said conversion    processing that is applied to said unsharp image signals control    averaging of image signals.

By applying this configuration, unsharpness is controlled in a highcontrast section, which is a cause of overshoot/undershoot andconsequently, effective edge emphasis and control of noise and artifactsare enabled.

-   (90) The image process apparatus, wherein said conversion processing    that is applied varies depending on pixel values of said unsharp    image signals to be processed by said conversion processing.

By applying this configuration, processing depending on signal values ofunsharp image signals become possible and by enhancing control ofartifacts of signals with noticeable noise signal (density), moreeffective edge emphasis and control of noise and artifacts are enabled.

-   (91) The image processing apparatus, wherein said conversion    processing that is applied to said unsharp image signals varies    depending on pixel values of said unsharp images at lowest frequency    band.

By applying this configuration, changes of conversion of unsharp imagesignals may follow a major configuration of an original image signal.

-   (92) The image processing apparatus wherein said conversion    processing that is applied to said unsharp image signals varies    depending on pixel values of said original signal.

By applying this configuration, changes of conversion of unsharp imagesmay follow an original image signal faithfully.

-   (93) The image processing apparatus, wherein a degree of averaging    control of said conversion processing that is applied to said    unsharp image signals increases as frequency bands of said unsharp    images become lower.

By applying this configuration, a degree of compensation increases asfrequency bands become lower and consequently, image signals of higherquality may be obtained.

-   (94) The image processing apparatus, wherein a degree of averaging    control of said conversion processing that is applied to said    unsharp image signals increases as frequency bands of said unsharp    image signals become higher.

By applying this configuration, averaging control increases for highfrequency component signals that tend to contain many noise componentsignals and consequently effective edge emphasis and control of noiseand artifacts are enabled.

-   (95) The image processing apparatus that obtains processed image    signals by adding compensation signals that are obtained from low    frequency component signals of an original signal to said original    signal or a low frequency image signal comprising multiple pixels,    wherein said compensation signals are obtained by applying    conversion processing to unsharp image signals of multiple frequency    bands that are generated from said original signal, generating    high-frequency image signals that are obtained adding differential    image signals that are obtained by differences between said unsharp    image signals and said image signals generated after said conversion    processing, and obtaining differences of said low frequency image    signals from results of conversion of low frequency image signals    that are obtained from differences between said high frequency image    signals and said original image signal.

By applying this configuration, a compensation section that is added toan original image signal or super low frequency image signal, processingimage signals can be generated by applying both image signal dynamicrange compression and control of noise and artifacts.

-   (96) The image processing apparatus that obtains processed image    signals by generating unsharp image signals of multiple frequency    bands for an original image signal consisting of multiple pixels,    applying conversion processing to differential image signals of said    unsharp image signals, and adding to an original image signal or    lowest frequency image signals, or adding a compensation signal that    is calculated from a low frequency component signal that is obtained    from a difference between said original image signal and a result of    multiplication of a differential signal after said conversion    processing to an original image processing or a lowest frequency    image processing, wherein said conversion processing varies    depending on pixel values of said unsharp image signals.

By applying this configuration, emphasis of bands containing many noisesin signal areas where noises are noticeable can be controlled byadjusting differential image signals that are added to an original imagesignal or lowest frequency band image signals, depending on signalvalues of unsharp images and consequently, more effective edge emphasisand control of noise and artifacts are enabled.

-   (97) The image processing apparatus, wherein said differential image    signal indicate a difference between unsharp image signals of a pair    of adjacent frequency bands or a difference between an original    image signal and a converted unsharp image signal.

By applying this configuration, a frequency band overlapping section ofeach differential signal is reduced by determining a difference betweena pair of adjacent image signals and operation by band units is enabledby matching conversion processing to unsharp image signals.

-   (98) The image processing apparatus, wherein said unsharp image    signal on which said conversion processing depends is an image    signal of either of both of said image signals used when said    differential signals were obtained.

By applying this configuration, an unsharp image signal of an image sizeidentical to a converted image signal can be used when a pyramidalgorithm is used also and consequently, processing can be simplified.

-   (99) The image processing apparatus, wherein conversion processing    that is applied to said multiple differential image signals varies    depending on said differential image signals.

By applying this configuration, adjustments depending on frequency bandsare enabled and consequently more effective edge emphasis and control ofnoise and artifacts are enabled.

-   (100) The image processing apparatus, wherein conversion processing    that is applied to said differential image signals controls absolute    values of pixel values in at least some image signals.

By applying this configuration, emphasis on a high contrast section,which is a cause of overshoot/undershoot is controlled and consequently,more effective edge emphasis and control of noise and artifacts areenabled.

-   (101) The image processing apparatus, wherein control of absolute    values of image signals by conversion processing that is applied to    said differential image signals increases as frequency bands of said    differential image signals become lower.

By applying this configuration, the lower the frequency band in thedifferential image signal, the greater the control of the absolute valuebecomes, enabling generation of sharper image signals with control ofnoise and artifacts more effectively.

-   (102) The image processing apparatus, wherein control of absolute    values of image signals by conversion processing that is applied to    said differential image signals increase as frequency bands of said    differential image signals become higher.

By applying this configuration, control over absolute values increasesas a frequency section that tends to contain many noise componentsignals becomes high and consequently, more effective edge emphasis andcontrol of noise and artifacts are enabled.

-   (103) The image processing apparatus, wherein a conversion function    that actualizes given frequency characteristics is determined by    specifying frequency characteristics and processing is performed by    said conversion function that was determined.

By applying this configuration, users only need to specify requiredfrequency characteristics without having to be aware of variousparameters to be set and consequently, processing is simplified.

-   (104) The image processing apparatus, wherein specification of said    frequency characteristics can be changed according to a density.

By applying this configuration, users can easily specify processingdepending on signal values such as control of noise emphasis byoperating frequency characteristics of signal areas containingnoticeable noise.

-   (105) The image processing apparatus, wherein specification of said    frequency characteristics can be changed depending on a density for    each of unsharp image signals or differential image signals.

By applying this configuration, users can easily set an intensity ofprocessing depending on signal values for each frequency band.

-   (106) The image processing apparatus, wherein a set of parameters is    specified in said frequency characteristic processing and processing    can be specified by selecting said set of parameters.

The image processing apparatus, wherein users can select an optimumparameter set easily without manipulating many parameters.

-   (107) The image processing apparatus that obtains processed image    signals by generating multiple unsharp image signals for an original    image signal consisting of multiple pixels and adding compensation    signals that are obtained by adding a differential signal between    said original image signal and said unsharp image signal or a    differential image signal between said unsharp image signal and    another said unsharp image signal to said original image signal or a    lowest frequency image signal for said original image signal or    calculating a difference of a result of multiplication of said    differential image signal, to said original image signal or said    lowest frequency image signal, wherein frequency characteristics of    processing image signals are changed by changing mask frequencies    used for mask processing for generating said unsharp image signals.

By applying this configuration, more delicate frequency characteristicadjustments are enabled for processing images by changing mask frequencycharacteristics.

-   (108) The image processing apparatus, wherein said mask processing    is specific filter repetition processing.

By applying this configuration, frequency characteristics can beadjusted at high speed without using multiple filters.

-   (109) The image processing apparatus, wherein a mask of said    repetition processing is a simple average.

By applying this configuration, frequency characteristics can beadjusted at high speed.

-   (110) The image processing apparatus, wherein a mask of said    repetition processing is a simple average of 2 pixels×2 pixels.

By applying this configuration, unsharp image signals can be generatedat high speed and also according to normal distribution.

-   (111) The image processing apparatus, wherein frequency    characteristics of said processing image signals are specified by a    processing repetition count of said repetition processing.

By applying this configuration, frequency characteristics can bespecified easily.

-   (112) The image processing apparatus, wherein frequency    characteristics of said processing image signals are specified by    designating a weight of a mask at generation of unsharp image    signals using a variance value of normal distribution and processing    is performed by calculating said mask processing repetition count    approximating with normal distribution of said variance value that    was specified.

By applying this configuration, frequency characteristics can bespecified easily.

-   (113) The image processing apparatus, wherein said mask processing    varies depending on said unsharp image signal.

By applying this configuration, frequency characteristics may beadjusted according to a frequency band.

-   (114) The image processing apparatus, wherein said mask processing    varies depending on an original image signal.

By applying this configuration, frequency characteristics may beadjusted according to a type of an original image signal, for instance,body parts to be examined.

-   (115) The image processing apparatus, wherein said masking    processing varies depending on frequency characteristics of said    original image signal.

By applying this configuration, adjustments according to frequencycharacteristics of an original image signal are enabled for controllingfrequency bands with excessive noise.

-   (116) The image processing apparatus that obtains processed image    signals by generating multiple unsharp image signals of different    resolutions using a pyramid algorithm for an original image signal    consisting of multiple pixels and adding a differential image signal    between said original image signal and said unsharp image signal or    a differential signal between said unsharp image signals to an    original image signal or a lowest frequency image signal, or adding    a compensation signal obtained by calculating a difference of a    result of adding or sum of said differential image signals to an    original image signal or a lowest frequency image signal, wherein    frequency characteristics of image processing signals are changed by    a changing interpolation-processing method for addition or    subtraction of said image signals of different resolutions.

By applying this configuration, more delicate frequency characteristicadjustments of processing image signals are enabled by changingfrequency of interpolation-processing.

-   (117) The image signal apparatus, wherein said    interpolation-processing is performed based on a sampling function    of an original image signal.

By applying this configuration, frequency characteristics of unsharpimage signals can be reproduced more faithfully.

-   (118) The image signal apparatus, wherein said    interpolation-processing performs linear interpolation.

By applying this configuration, processing can be preformed at a highspeed without making major changes in frequency characteristics ofunsharp image signals.

-   (119) The image signal apparatus, wherein said    interpolation-processing is spline interpolation.

By applying this configuration, smooth interpolation is achieved.

-   (120) The image signal apparatus, wherein said    interpolation-processing varies depending on frequency bands of    interpolation image signals.

By applying this configuration, frequency characteristics may beadjusted for each frequency band.

-   (121) The image signal apparatus, wherein said    interpolation-processing varies depending on an original image    signal.

By applying this configuration, frequency characteristics may beadjusted according to a type of an original image signal, for instancebody parts examined.

-   (122) The image signal apparatus, wherein said    interpolation-processing varies according to frequency    characteristics of an original image signal.

By applying this configuration, adjustments may be made according tofrequency characteristics of an original image signal such ascontrolling of frequency bands with many noise signals.

-   (123) The image signal apparatus that obtains processed image    signals by generating multiple unsharp image signals of different    resolutions using a pyramid algorithm for an original image signal    consisting of multiple pixels and adding a differential signal    between said original image signal and said unsharp image signal or    a differential image signal between two of said unsharp image    signals to an original image signal or a lowest frequency image    signal, or adding a compensation signal that is obtained by    determining a difference of multiplication of said differential    image signals to an original image signal or a lowest frequency    image signal, wherein unsharp image signals are generated by mask    processing through said pyramid algorithm and a reduction rate of    unsharp image signals by down sampling changes according to mask    frequency characteristics.

By applying this configuration, a processing speed may be increasedefficiently by changing a reduction rate of image signals depend onfrequency characteristics of a mask.

-   (124) The image processing apparatus, wherein said mask processing    varies depending on unsharp image signals.

By applying this configuration, frequency characteristics may beadjusted according to a frequency band.

-   (125) The image processing apparatus, wherein said mask processing    varies depending on an original image signal.

By applying this configuration, frequency characteristics may beadjusted according to a type of an original image, for instance, bodyparts examined.

-   (126) The image processing apparatus, wherein said mask processing    varies depending on frequency characteristics of an original image    signal.

By applying this configuration, adjustments may be made according tofrequency characteristics of an original image signal such ascontrolling of frequency bands with many noise signals.

-   (127) The image processing apparatus, wherein changes of said    frequency characteristics of a mask or changes of    interpolation-processing are determined by specified frequency    characteristics.

By applying this configuration, users may easily generate image signalsof required frequency characteristics by determining characteristics offilters from said frequency characteristics.

-   (128) The image processing apparatus, wherein specification of said    frequency characteristics may be changed according to a density of    an original image signal or an unsharp image signal.

By applying this configuration, frequency characteristics may beadjusted effectively such as controlling of emphasis on signal areaswhere noise is noticeable.

-   (129) The image processing apparatus, wherein specification of said    frequency characteristics may be changed according to a density of    an original image signal or an unsharp image signal for each of said    unsharp image signals or a differential image signal.

By applying this configuration, frequency characteristics may beadjusted efficiently such as controlling of emphasis of signal areaswhere noise is noticeable in afrequency bands containing many noisesignals.

-   (130) The image processing apparatus that retains a set of    parameters required for processing said frequency characteristics,    wherein processing is specified by selecting said set of parameters.

By applying this configuration, users may achieve optimum processing byspecifying a set of parameters without setting detailed parameters.

-   (131) The image processing apparatus that obtains processed image    signals by generating multiple unsharp image signals for an original    image signal consisting of multiple pixels and adding a differential    image signal that is generated from said unsharp image signal or    said original image signal to an original image signal or a lowest    frequency image signal, or adding a compensation signal derived from    a difference of multiplication of said differential image signals to    said original image signal or said lowest frequency image signal,    wherein filtering processing for generating said unsharp image    signals is repetition of specific filters.

By applying this configuration, processing may be simplified.

-   (132) The image processing apparatus, wherein a mask of said    repetition processing is a simple average.

By applying this configuration, processing may be simplified and aprocessing speed may be increased.

-   (133) The image processing apparatus, wherein a mask of said    repetition processing is a simple average of 2 pixels×2 pixels.

By applying this configuration, effects equivalent to those achievedfrom processing by a weighting mask according to Gaussian distributionmay be obtained.

-   (134) The image processing apparatus, wherein said mask processing    varies depending on an unsharp image.

By applying this configuration, frequency characteristics may beadjusted according to a frequency band.

-   (135) The image processing apparatus, wherein said mask processing    varies depending on an original image signal.

By applying this configuration, frequency characteristics may beadjusted according to a type of an original image, for instance, bodyparts examined.

-   (136) The image processing apparatus, wherein said mask processing    varies depending on frequency characteristics of said original image    signal.

By applying this configuration, processing may be varied according tofrequency characteristics of an original image signal such ascontrolling of frequency bands containing many noise signals.

-   (137) The image processing apparatus, wherein a repetition count of    said single average of 2×2 is 16 or greater.

By applying this configuration, frequency band areas contained in eachunsharp image signal are reduced to about a half of frequency bandsbefore application of mask processing, enabling disassembly to anoptimum frequency band.

-   (138) The image processing apparatus, wherein a repetition count of    said simple average of 2×2 is 8 or greater.

By applying this configuration, frequency bands contained in eachunsharp image signal are reduced to about a half of frequency bandbefore application of mask processing, enabling disassembly to anoptimum frequency band.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the present invention will becomeapparent upon reading the following detailed description and uponreference to the drawings in which:

FIG. 1 shows a block diagram of the first embodiment of this inventionthat performs frequency-emphasizing processing;

FIG. 2 shows compensation functions of unsharp image signals;

FIG. 3 shows changes of compensation functions related to densities;

FIG. 4 shows a block diagram of the first embodiment of this inventionthat performs dynamic range compression processing;

FIG. 5 shows a block diagram of the second embodiment of this inventionthat performs frequency-emphasizing processing;

FIG. 6 shows changes of frequencies of conversion functions ofdifferential image signals;

FIG. 7 shows changes of densities of conversion functions ofdifferential image signals;

FIG. 8 shows a block diagram of the second embodiment of this inventionthat performs dynamic range compression processing;

FIG. 9 shows a diagram for specification of frequency characteristics;

FIG. 10 shows a diagram for specification of density-dependent emphasis;

FIG. 11 shows a block diagram of a configuration of a decompositionsection that executes a pyramid algorithm;

FIG. 12 shows low pass filters;

FIG. 13 shows various sizes of output image signals of a pyramidalgorithm section;

FIG. 14 shows a block diagram of a configuration example of therestruction section that executes a pyramid algorithm;

FIG. 15 shows a filter processing;

FIG. 16 shows 2×2 simple average filters;

FIG. 17 shows a relationship between distribution of simple averagefilter repetition count 8 and Gaussian distribution;

FIG. 18 shows mask examples;

FIG. 19 shows an explanatory illustration for a number of filtering andresponses;

FIG. 20 shows an explanatory illustration of a conventional frequencyemphasizing processing;

FIG. 21 shows an explanatory illustration of a conventional dynamicrange compression processing; and

FIG. 22 shows an explanatory illustration of a compensation componentcalculation.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 is a block diagram showing the first embodiment of this inventionthat uses frequency-emphasizing processing. The apparatus shown in thediagram generates unsharp image signals of multiple frequency hands oran original image signal consisting of multiple pixels and obtainsprocessed image signals by adding a differential image signal with theunsharp image signal to the original image signal or a lowest frequencyimage signal.

In the diagram, 6 indicates a filter-processing section that applies thefiltering processing, 10 indicates an unsharp image signal generatingsection that generates unsharp image signals after receiving output fromfilter processing section 6, 11 indicates a conversion processingsection that applies conversion processing to unsharp image signals thatwere generated in unsharp image generating section, 12 indicates adifferential processing section that finds a difference between anoriginal image signal and a converted image signal and a differencebetween an unsharp image signal and a converted unsharp image signal, 13indicates an addition processing section that adds differential imagesignals that were obtained in the differential processing section.Filter processing section 6, unsharp image generating section 10,conversion processing section 11, differential processing section 12,and addition processing section 13 can be implemented by hardware orsoftware. Operation of the apparatus in this configuration is describedbelow.

Filter processing section 6 applies filtering processing as describedbefore after receiving an original image signal. Unsharpimage-generating section 10 receives output from filter processingsection 6 and generates unsharp image signals using a pyramid algorithm,for example. Unsharp image-signal generating section 10 generatesunsharp image signals of multiple frequency bands of different frequencycharacteristics. Conversion processing section 11 performs conversionprocessing for the unsharp image signals that were obtained. All theconversion processing technologies that are publicly available can beused for the conversion processing.

Differential processing section 12 produces a difference between theconverted unsharp image signal that was obtained as described above andthe original image signal, and a difference between the unsharp imagesignal and the converted unsharp image signal. The differential imagesignal that is obtained here is a difference between two unsharp imagesignals in a pair of adjacent frequency bands or a difference betweenthe original image signal and the converted unsharp image signal.Addition processing section 13 obtains a high frequency component signalby adding the differential image signal that was obtained indifferential processing section 12. A processed image signal can beobtained by adding the high frequency component signal to the originalimage signal or the lowest frequency image signal, one of the actualprocessing units in conversion processing section 11 is described below.FIG. 2 shows compensation functions of unsharp images. Horizontal axis xrepresents signal values that indicate differences between signal valuesbefore application of unsharp processing and signal values afterapplication of unsharp processing. Vertical axis y representscompensating component signals, where the upper section is allocated fora function that indicates compensating component signals in a lowfrequency band and the lower section is allocated for a function thatindicates compensating component signals in a high frequency band. Thesecharacteristics have a feature for compensating more in an area ofgreater contrast. The compensating component signal that is obtainedconsequently is processed by image conversion by being added to anunsharp image signal.

As shown in FIG. 3, the function varies according to the density ofunsharp image signals. In FIG. 3, horizontal axis x represents signalvalues that indicate a difference between a signal value beforeapplication of unsharp processing and a signal value after applicationof unsharp processing and vertical axis y represents compensatingcomponent signals. The upper section is allocated for the function thatindicates compensating component signals in a low density area and thelower section is allocated for the function that indicates compensatingcomponent signals in a high density area. Conversion in conversionprocessing 11 in the embodiment has both characteristics shown in FIG. 2and FIG. 3. As a result, the lower the density or the lower thefrequency band, the closer the image signal is to the unsharp imagesignal one above or the original image signal.

As a result, the frequency emphasis in the low density area is weakenedand consequently grain deterioration can be controlled. When the pixeldifference with the unsharp image signal or the original image signalbecomes large by averaging, overshoot/undershoot of the emphasizingimage signal can be controlled by adding processing that controlsaveraging that approximates the pixel to the image one above or theoriginal image. By increasing the enhancement of the compensation as thefrequency band becomes lower, sharper and better image signals can beobtained.

The configuration in FIG. 1 that is shown above is actualized as theconfiguration excluding filter processing section 6.

According to the configuration excluding filter processing section 6 adifferential image signal that is added to an original image signal isadjusted by conversion of an unsharp image signal and consequently, aprocessed image signal with edge emphasis and control of noise andartifacts may be generated.

Since said differential image signal is a difference between two unsharpimage signals of a pair of adjacent frequency bands or a differencebetween an original image signal and a converted unsharp image signal,frequency band overlapping sections of differential image signalsdecrease, enabling operation of each band by matching conversionprocessing to the unsharp image signal.

In the conversion processing that is applied to said unsharp imagesignals of said multiple frequency bands can perform effective edgeemphasis and control of noise and artifacts, by converting pixel valuesof unsharp image signals based on non-linear conversion.

Since the conversion processing that is applied to unsharp image signalsof said multiple frequency bands is determined through the originalimage signal or the unsharp image signals of the multiple frequencybands, processing according to the unsharping tendency of image signalscan be performed, enabling more effective edge emphasis and control ofnoise and artifacts.

Since the conversion processing that is applied to unsharp image signalsof said multiple frequency bands is determined by unsharp image signalsof a pair of adjacent frequency bands or an original image signal,processing according to the unsharping tendency of image signals can beperformed, enabling more effective edge emphasis and control of noiseand artifacts.

Since the conversion processing that is applied to unsharp image signalsof said multiple frequency bands vary according to the other unsharpimage signal that is used for generating said differential image signalor the pixel value of original image signal, processing can be performedaccording to the pixels before application of unsharp processing,enabling conversion considering high frequency component signals andthus more effective edge emphasis and control of noise and artifacts.

Since the conversion processing that is applied to unsharp image signalsof said multiple frequency bands varies according to said unsharp imagesignals, adjustments can be made according to the frequency band,enabling more effective edge emphasis and control of noise andartifacts.

Since conversion processing that is applied to said unsharp imagesignals controls averaging of image signals, unsharping in the highcontrast section, which is the cause of overshoot/undershoot, iscontrolled, enabling more effective edge emphasis and control of noiseand artifacts.

Since conversion processing that is applied to said unsharp imagesignals varies according to the pixel values of the unsharp imagesignals, processing can be performed according to the signal values ofthe unsharp image signals and by enhancing control of artifacts of thesignal areas with noticeable noise (density), more effective edgeemphasis and control of noise and artifacts are enabled.

Since conversion processing that is applied to said unsharp imagesignals varies according to the pixel value of the unsharp image oflowest frequency band, conversion variations of the unsharp imagesignals may follow the major structure of the original image signal.

Since conversion processing of said unsharp image signals variesaccording to the pixel value of the original image signal, variations ofthe unsharp image signals faithfully follow the original image signal.

In the conversion processing that is applied to said unsharp imagesignals, by increasing the tendency of averaging control as thefrequency band of the unsharp image signal becomes lower, averagingsuppression can be increased as the frequency band becomes lower,enabling generation of sharper image signals with noise and artifactscontrol

In the conversion processing that is applied to said unsharp imagesignals, by increasing the averaging control as the frequency band ofthe unsharp image signal becomes higher, averaging supression can beincreased as the number of noise component signals contained in the highfrequency component containing becomes higher, enabling effective edgeemphasis and control of noise and artifacts.

The block diagram in FIG. 4 shows the first embodiment of this inventionusing dynamic range compression processing. This apparatus configures animage processing apparatus that obtains a processed image signal bycalculating a compensation signal from the low frequency componentsignal that is obtained by producing a difference between an originalimage signal and an added result of said differential image signals, andadding the compensation signal to the original image signal or a lowestfrequency image signal. For the same items as the items in FIG. 3, thesame signs in FIG. 3 are assigned.

In the diagram, 6 indicates a filter processing section that appliesfiltering processing to an original image signal as described above, 10indicates an unsharp image generating section that receives output fromfilter processing section 6 and generates unsharp image signals from theoriginal image signal, 11 indicates a conversion processing section thatapplies conversion processing to the image signals that were generatedin unsharp image generating section 10, 12 indicates a differentialprocessing section that indicates a different between an image signaland converted unsharp image signal and a difference between unsharpimage signal and a converted unsharp image signal, 14 indicates acompensation signal calculation section that calculates a compensationsignal from the low frequency component signal that is obtained bysubtracting the high frequency component signal obtained by summing thedifferential image signals that was obtained in differential processingsection 12 from the original image signal, 15 indicates a compensationsignal adding section that adds the compensation signal that wasobtained in compensation signal calculating section 14. Filterprocessing 6, unsharp image signal generating section 10, conversionprocessing section 11, differential processing section 12, compensationsignal calculating section, compensation signal calculation section 14,and compensation signal adding section 15 can be processed by hardwareor software. The operation of the apparatus in this configuration isdescribed below.

Filter processing section 6 applies filtering processing after receivingan original image signal as described above. Unsharp image signalgenerating section 10 generates receives output from filter processingsection 6 and generates by unsharp image signals through a pyramidalgorithm, for instance. Unsharp image signals of multiple frequencybands of different frequency characteristics are obtained from unsharpimage signal generating section 10. Conversion processing 11 covertsunsharp image signals that were obtained. All the conversion processingtechnologies available publicly can be used for the conversionprocessing.

Differential processing section 12 obtains signal values that indicate adifference between the conversion image signal that is obtained asdescribed above and the original image signal and a difference betweenthe unsharp image signal and the conversion image signal. Thedifferential image signal indicates a signal value that indicates adifference between the two unsharp image signals of a pair of adjacentfrequency bands or a difference between the original image signal andthe converted unsharp image signal. Then, compensation signalcalculating section 14 calculates compensation signals for thedifferential image signals that were obtained in differential processingsection 12.

Compensation component signals are determined as shown in FIG. 22, forinstance. FIG. 22 shows compensation component signals in dynamic rangecompression of a low density section. In the diagram, the horizontalaxis is allocated to signal values (x) of unsharp image signals and thevertical axis is allocated to compensation component signals F(x). Asshown in FIG. 22, the lower the signal value, the larger thecompensation component signal is calculated and the signal is added tothe original image signal. Compensation signal adding section 15 addsthe compensation signal that was obtained in compensation signalcalculating section 14 and obtains a processed image signal by addingthe signal to the original image signal.

For this embodiment also, each unsharp image signal can be converted tothe function that is shown in FIG. 2. Horizontal axis x represents asignal value that indicates a difference between the signal value beforeapplication of unsharp processing and the signal value after applicationof unsharp processing. This function varies according to the density ofthe unsharp image signal as shown in FIG. 3. In FIG. 3, horizontal axisx represents signal values that indicate differences and vertical axis yrepresents compensation component signals.

As described above, by enhancing a compensation component as the densitybecomes lower, the unsharp image signal becomes an image signal with ahigh frequency component signal added at a low density area. As aresult, the differential image signal no longer contains high frequencycomponent signals in the area equivalent to a low density. Therefore, alow frequency image signal that is obtained by subtracting thedifferential signal from the original image signal contains a highfrequency in a low density section. When the dynamic range of the lowfrequency image signal is compressed, the high frequency componentsignal is also compressed and consequently, grain deterioration bydynamic range compression in the low density section can be controlled.

When the pixel value with the image signal one above or with theoriginal image signal is large due to averaging, overshoot/undershootafter processing can be controlled by adding processing for controllingaveraging that approximates the pixel value to that of the image signalone above or the original image signal. By enhancing this compensationas the frequency band becomes lower, sharper and better image signalswith less artifacts and noise can be obtained.

The averaging adjustment in the low density section described above isnot required for all the unsharp image signals. By making the adjustmentfor only unsharp image signals of comparatively high frequency, noisecontrol and emphasis of edge component signals can be adjusted. Theadjustment depending on the density can be varied according to theunsharp image signal. For instance, by enhancing averaging control inthe low density section as the frequency of unsharp image signalsbecomes higher, image signals can be generated by emphasizing the edgeportions included in the original image adequately and controlling graindeterioration.

Filter processing section 6 can also be excluded from the configurationshown in FIG. 4, creating another configuration.

In the embodiment excluding filter processing section 6, thecompensation section that is added to the original image signal or thesuper low frequency image signal is adjusted by converting the unsharpimage signal, enabling generation of processing image signals byapplying both dynamic range compression of image signals and control ofnoise and artifacts.

Since said differential image signal indicates a difference between twounsharp image signals of a pair of adjacent frequency bands or betweenan original image signal and a converted unsharp image signal, frequencyband overlapping sections of each differential image signal are reducedby producing a difference between adjacent unsharp signals, enablingoperation of each band by matching conversion processing to the unsharpimage signal.

Since the conversion processing that is applied to the unsharp imagesignals of said multiple frequency bands converts pixel values of theunsharp image signals, noise and artifacts can be controlledeffectively.

Since the conversion processing that is applied to the unsharp imagesignals of said multiple frequency bands are determined by the originalimage signal or the unsharp image signals of the multiple frequencybands, processing according to the unsharping tendency of image signalscan be performed, enabling more effective control of noise andartifacts.

Since the conversion processing that is applied to the unsharp imagesignals of said multiple frequency bands are determined by the unsharpimage signals of adjacent frequency bands or the original image signal,processing according to the unsharping tendency of image signals can beperformed, enabling more effective control of noise and artifacts.

Since the conversion processing that is applied to the unsharp imagesignals of said multiple frequency bands varies according to the pixelvalue of the other unsharp image signal that is used for generating saiddifferential image signal or the original image signal, processingaccording to the pixel before application of unsharping processing canbe performed, enabling conversion processing considering higherfrequency components signals and consequently enabling more effectiveedge emphasis and control of artifacts and noise.

Since the conversion processing that is applied to the unsharp imagesignals of said multiple frequency bands varies according to the saidunsharp image signals, adjustments according to the frequency bands canbe made, enabling more effective control of noise and artifacts.

Since the conversion processing that is applied to the said unsharpimage signals controls averaging of image signals, unsharpness iscontrolled in the high contrast area, which is the cause ofovershoot/undershoot, enabling more effective control of noise andartifacts.

Since the conversion processing that is applied to said unsharp imagesignals varies according to the pixel values of the unsharp imagesignals, the processing according to the signal values of the unsharpimage signals can be performed, enabling averaging control in the signalarea with noticeable noise and thereby enabling more effective controlof noise and artifacts.

Since the conversion processing that is applied to said unsharp imagesignals varies according to the pixel values of the unsharp imagesignals of the lowest frequency band, conversion variation of theunsharp image signals can follow the major structure of the originalimage signal.

Since the conversion processing that is applied to said unsharp imagesignals varies according to the pixel value of the original imagesignal, conversion variation of the unsharp images can follow theoriginal image signal faithfully.

In the conversion processing that is applied to said unsharp imagesignals, averaging suppression can be enhanced as the frequency bandbecomes lower by enhancing the averaging suppression tendency as thefrequency band of the unsharp image signals become lower, therebyenabling more effective generation of sharper images with control ofnoise and artifacts.

Since in the conversion processing that is applied to said unsharp imagesignals, averaging suppression is enhanced as the frequency band becomeshigher, the averaging suppression is more enhanced as a higher frequencyband that tends to contain more noise component signals, enabling moreeffective control of noise and artifacts.

This invention enables linear conversion for differential image signalsthat are a difference between an original image signal and an unsharpimage signal and a difference between two unsharp image signals, usingthe density information of the unsharp image signals or the originalimage signal. The block diagram in FIG. 5 shows the second embodiment ofthis invention using frequency emphasizing processing. For the itemsidentical to the items in FIG. 1, the same signs are assigned. Theapparatus shown in the diagram configures an image processing apparatusthat obtains processed image signals by generating unsharp image signalsof multiple frequency bands for an original image consisting of multiplepixels, applying conversion processing for the differential imagesignals of said differential image signals, and adding to the originalimage signal or the lowest frequency.

In the diagram, 6 indicates a filtering processing section that appliesfiltering processing to an original image signal as described above, 10indicates an unsharp image signal generating section that generatesunsharp image signals from an original image signal, 12 indicates adifferential processing section that produces a difference between anoriginal image signal and an unsharp image signal and a differencebetween a pair of two adjacent unsharp image signals, 16 indicates adensity-dependent conversion processing section that performsdensity-dependent conversion processing for the differential imagesignals that were obtained in said differential processing section 12,17 indicates an addition processing section that adds the conversionimage signal obtained in said density-dependent conversion processing16. Filter processing section 6, unsharp image signal generating section10, differential processing section 12, density-dependent conversionprocessing section 16, and addition processing section 17 can beachieved either by hardware or software. The operation of the apparatuswith this configuration is described below.

Filter processing section 6 applies filtering processing as describedabove after receiving an original image signal. After receiving outputfrom filter processing section 6, unsharp image signal generatingsection 10 generates unsharp image signals using a pyramid algorithm.Unsharp image signal generating section generates unsharp image signalsof multiple frequency bands of different frequency characteristics.Differential processing section 12 obtains a difference between theoriginal image signal and the unsharp image signal that were obtained asdescribed and a difference between a pair of adjacent unsharp imagesignals.

Density-dependent conversion processing section 16 performsdensity-depending conversion processing for the differential imagesignal that is provided from differential processing section 12. FIG. 6and FIG. 7 illustrate conversion characteristics of density-dependentconversion processing 16 and FIG. 6 illustrates changes of frequenciesby the conversion function of differential image signals and FIG. 7illustrates changes by the density.

In FIG. 6, horizontal axis x is allocated to pixel values that indicatethe differences and vertical axis y indicates pixel values of converteddifferential image signals. In FIG. 7, horizontal axis x is allocated tosignal values that indicate the differences and vertical axis y isallocated to pixel values of converted differential image signals.Density-dependent conversion processing section 16 has a functioncontaining both the characteristics shown in FIG. 6 and thecharacteristics shown in FIG. 7. In FIG. 6, the function that indicatesconversion in high frequency characteristics belong to the upperfunction and the function that indicates conversion in low frequencycharacteristics belong to the lower function. In FIG. 7 that indicates aconversion function, the upper function indicates the characteristics,whereby unsharp images for generating differential image signals are ina high density and the lower function indicates the characteristics,whereby unsharp images used for generating differential image signalsare in a low density.

In this case, since the image size of the differential image signal andthe image size of the unsharp image signal match, the pixel value of theunsharp image signal corresponding to the pixel value of thedifferential image signal can be obtained easily. In this case, in thenon-linear function, artifacts such as overshoot and undershoot can beeliminated by suppresing the signals in the section with largedifferential values as shown in FIGS. 6 and 7. By enhancing supress ofdifferential component signals in sections where the frequency bands andthe density become lower, sharper and better image signals can beobtained with less artifacts and noise.

Addition processing section 17 performs addition processing for thesignal that was converted as described above. Then, the section adds theaddition signal to the original image signal. As a result, by applyingdensity-dependent linear conversion to the differential image signal,the high frequency component signal that is added to the original imagesignal is adjusted, enabling generation of processing image signals withedge emphasis and control of noise and artifacts.

FIG. 8 shows a block diagram of the second embodiment of this inventionthat uses dynamic range compression processing. For the same items asthe items in FIG. 4 and FIG. 5, the same signs in FIG. 4 and FIG. 5 areassigned. The apparatus that is shown in the diagram configures an imageprocessing apparatus that obtains a processed image signal by adding thecompensation signal that is obtained from an original image signal tosaid original image signal consisting of multiple pixels or a lowfrequency image signal.

In the diagram, 6 indicates a filtering processing section that appliesfiltering processing to an original image signal as described above, 10indicates an unsharp image signal generating section that generatesunsharp image signals from an original image signal, 12 indicates adifferential processing section that produces a difference between anoriginal image signal and an unsharp image signal and a differencebetween a pair of two adjacent unsharp image signals, 16 indicates adensity-dependent conversion processing section that performsdensity-dependent conversion processing for the differential imagesignals that were obtained in said differential processing section 12,14 indicates a compensation signal calculating section that calculates acompensation signal from the high frequency component signal obtained bysubtracting from the original image signal the high frequency signalthat is obtained by multiplying the conversion image signal that wasobtained in the density-dependent conversion processing section 16, 15indicates a compensation signal adding section that adds thecompensation signal that is obtained in the compensation signalcalculating section. Filter processing section 6, unsharp image signalgenerating section 10, differential processing section 12,density-dependent conversion processing section 16, compensation signalcalculating section 14, and compensation signal adding section 15 can beachieved by either hardware or software. The operation of the apparatusin this configuration is described below.

Filter processing section 6 applies filtering processing as describedabove after receiving an original image signal. Unsharp image signalgenerating section 10 generates unsharp image signals for the originalimage signal 10, using a pyramid algorithm for instance. Unsharp imagesignal generating section generates unsharp image signals of multiplefrequency bands of different frequency characteristics. Differentialprocessing section 12 obtains a difference between the conversion imagesignal that was obtained as described above and the original imagesignal and a difference between a pair of adjacent unsharp imagesignals.

Density-dependent conversion processing section 16 performsdensity-dependent conversion processing for the differential imagesignal that was passed from differential processing section 12. Theconversion characteristics that are shown in FIG. 6 and FIG. 7 are usedfor this section. Compensation signal calculating section 14 calculatesa compensation signal from the low frequency component signal that isobtained by subtracting the high frequency component signal that isobtained by multiplying the conversion image signals that were obtainedby density-dependent conversion processing 16 from the original imagesignal. Compensation signal adding section 15 adds the compensationsignal that is obtained in this way. A processed image signal isobtained by adding the compensation signal to the original image signal.

According to this embodiment, an adjustment is made to the highfrequency component signal that is added to the compensation componentsignal that is added to the original image signal or the super lowfrequency image signal by converting the differential image signal andcontrolling the component signal, and as a result, a processing imagesignal can be obtained by applying both image signal dynamic compressionand control of noise and artifacts.

This invention enables adjustments of more delicate frequencycharacteristics for processing image signals by changing the maskfrequency characteristics. Since said mask processing is specific filterrepetition processing, frequency characteristics can be adjusted at ahigh speed without using multiple filters.

Since the mask of said repetition processing is a simple average,frequency characteristics can be changed at a high speed.

Since the mask of said repetition processing is a simple average of 2pixels×2 pixels, unsharp image signals can be generated at a higherspeed and according to normal distribution.

Frequency characteristics of said processing image signal are specifiedby the repetition count of said repetition processing, enablingspecification of frequency characteristics easily.

Since frequency characteristics of said processing image signal arespecified by specifying the weight of the mask used at generation of anunsharp image signal with a variance value of normal distribution,frequency characteristics can be specified easily by performingprocessing by calculating the mask processing repetition count close tothe normal distribution of said specified variance value.

Since said interpolation-processing varies depending on the unsharpimage signal, frequency characteristics can be adjusted according to thefrequency band.

Since said interpolation-processing varies depending on the originalimage signal, frequency characteristics can be adjusted according to thetype of the original image signal, for instance body parts examined.

Since said interpolation-processing varies depending on the frequencycharacteristics of said original image signal, adjustments are enabledaccording to the frequency of the original image signal such assuppression of frequency bands with many noise signals.

Since this invention performs addition or subtraction between imagesignals of different resolution, frequency characteristics of processedimage signals can be changed by changing the interpolation-processingmethod.

In this configuration, more delicate adjustments of frequencycharacteristics of processed signals can be made by changing thefrequency characteristics of interpolation-processing.

In this case, since said interpolation-processing is specific filterrepetition processing, frequency characteristics can be adjusted at ahigh speed without using multiple filters.

Since the mask of said repetition processing is a simple average,frequency characteristics can be adjusted at a high speed.

Since the mask of the repetition processing is a simple average of 2pixels×2 pixels, interpolation-processing is enable at a high speedaccording to normalized distribution.

By specifying the frequency characteristics of said processed imagesignals through the repetition count of said repetition processing,frequency characteristics can be specified easily.

Since said interpolation-processing is based on the sampling function,frequency characteristics of the unsharp image signals can be reproducedmore faithfully.

Since said interpolation-processing is linear interpolation-processingis enabled at a thigh speed without making a major change in frequencycharacteristics of unsharp image signals.

Since said interpolation-processing spline interpolation, smoothinterpolation is enabled.

Since said interpolation-processing varies depending on the frequencyband of the interpolation of image signal, frequency characteristics canbe adjusted for each frequency band.

Since said interpolation-processing varies depending on the originalimage signal, frequency characteristics can be adjusted according to thetype of the original image signal. For instance, frequencycharacteristics can be adjusted according to the body part.

Since said interpolation-processing varies depending on the frequencycharacteristics of the original image signal, adjustments can be madeaccording to the frequency characteristics of the original image signalsuch as suppression of frequency bands with many noise signals

In this invention, a reduction rate of an unsharp image signal by downsampling of an image signal performed by said pyramid algorithm variesdepending on the frequency characteristics of the mask.

By applying this configuration, a processing speed can be increased moreefficiently by changing the reduction rate of the image signal throughthe frequency characteristics of the mask.

In this case, since said mask processing is specific filter repetitionprocessing, processing can be simplified.

Since the mask of said repetition processing is a simple average, theprocessing speed can be increased. Since the mask of said repetitionprocessing is a simple average of 2 pixels×2 pixels, unsharp imagesignals can be generated at a high speed according to the normaldistribution.

Since said mask processing varies depending on the unsharp image signal,frequency bands can be divided according to the processed image signal.

Since said mask processing varies depending on the original imagesignal, frequency characteristics can be adjusted according to the typeof the original image, for instance, body parts examined.

Since said mask processing varies according to the frequencycharacteristics of the original image, adjustments can be made accordingto the frequency characteristics of the original image such as suppressof frequency bands with many noise signals.

Since said differential image signal represents a difference between twounsharp image signals of a pair of adjacent frequency bands or adifference between an original image signal and a converted unsharpimage signal, frequency band overlapping sections of each differentialimage signal are reduced and operation for each band is enabled byemploying conversion processing to the image signal.

Since the unsharp image signal on which said conversion processingdepends is the image signal used for obtaining the differential imagesignal, an unsharp image signal of the same image size as the convertedimage signal can be used when a pyramid algorithm is used also, enablingprocessing simplification.

Since the conversion processing that is applied to said multipledifferential image signals varies depending on the differential imagesignal, adjustments can be performed according to the frequency band,enabling more effective edge emphasis and control of noise andartifacts.

Since the conversion processing that is applied to said differentialimage signal controls the absolute value of the pixel value in a part ofthe image signal, emphasis in a high contrast section, which is thecause of overshoot/undershoot is suppressed, enabling more effectiveedge emphasis and control of noise and artifacts.

Since in the conversion processing that is applied to said differentialimage signals, the lower the frequency band of the differential imagesignal, the greater the suppression of the absolute value of the imagesignal becomes, suppression of the absolute value increases as thefrequency band of the component signal of the differential image signalbecomes lower and as a result, sharer and better image signals with lessartifacts and noise can be obtained.

Since in the conversion processing that is applied to said differentialimage signals, the higher the frequency band of the differential imagesignal, the greater the control of absolute value of the image signalbecomes, control of the absolute value increases as the frequency bandbecomes higher, which tends to contain many noise signals, enabling moreeffective edge emphasis and control of noise and artifacts. Thehigh-frequency component signal in this invention refers to the signalthat is obtained by summing differential image signals.

The user interface functions that are provided to users are describedbelow. FIG. 9 shows an example of a specification method of frequencycharacteristics in this invention. In the diagram, the horizontal axisrepresents frequency bands and the vertical axis represents emphasizingdegrees. As shown in FIG. 9, the emphasizing degree of each frequencyband is specified by a to f in the diagram. The parameters can bespecified using the mouse or entering values. The ‘adjacent’ that isdescribed in the claim item refers to adjacent images when imagescontaining unsharp image signals or differential image signals arearranged from the highest frequency band. The frequency components thatare contained in differential image signals are not separated completelyand component signals that are contained may overlap. Alternatively,some frequency bands are not included, and mutually adjacent in terms ofa distance.

Emphasizing degrees regarding density can be specified in a graph asshown in FIG. 10. In the graph, the horizontal axis represents densitiesand the vertical axis represents emphasizing degrees. Frequencycharacteristics and density emphasizing degrees can be set in each bandor by determining the frequency characteristics of the entire area, therelationship between the density and emphasis common to all thefrequency bands that are emphasized can be set. In this graph, forinstance, emphasizing degrees in A and B are specified.

By specifying frequency characteristics in this way, a conversionfunction that achieves specified frequency characteristics is determinedand processing is performed by said conversion function, andconsequently users need simply to specify required frequencycharacteristics only, without having to be aware of various parametersto be set, enabling processing simplification.

Since said frequency characteristics can be changed according to thedensity, users can specify processing according to the signal value moreeasily, such as suppressing noise emphasis by manipulating frequencycharacteristics of the image signal area corresponding to the densitywith noticeable noise.

Since said frequency characteristics can be changed according to thedensity for each unsharp image signal or differential image signal,users can easily set the emphasis of processing according to the imagesignal value corresponding to the density.

By preparing a set of parameters required for said frequencycharacteristic processing and selecting the set of parameters, users canselect the most suitable parameter set easily without having to handlemany parameters.

Through this invention, an image processing apparatus that generatesprocessed image signals by generating multiple unsharp image signals foran original image signal consisting of multiple pixels, applyingconversion processing to the differential image signal generated fromsaid unsharp image signals or the original image signal, and adding thecompensation signal that is obtained by adding said differential imagesignal to the original image signal or a lowest frequency image signalor producing a difference of sum of said differential image signals. Inthis case, specific filter repetition processing for generating saidunsharp image signals is repetition of filters. As a result, processingcan be simplified.

In this invention, the mask of said repetition processing can be asimple average. By applying this configuration, processing can besimplified and the speed can be increased.

In this invention, the mask of said repetition processing can also be asimple average of 2 pixels and 2 pixels. As a result, effects equivalentto the processing by the weighting mask according to Gaussiandistribution can be achieved.

In this invention, said mask processing can be varied according to theunsharp image. As a result, processing can be performed according to thebody part.

In this invention, said mask processing can be varied according to theoriginal image signal. As a result, frequency characteristics can beadjusted according to the type of the original image, such as body partsexamined.

In this invention, said mask processing can be varied according to thefrequency of said original image signal. As a result, processing can bevaried according to the frequency of the image signal characteristicssuch as suppression of frequency bands with many noise signals.

In this invention, said repetition count of simple average of 2×2 can be16 or more. As a result, the frequency bands that are contained in eachunsharp image signal are reduced to about a half of the image signalbefore application of mask processing, enabling disassembling to anoptimum frequency band.

In this invention, said repetition count of simple average of 2×2 can be8 or more. As a result, the frequency bands contained in each unsharpimage signal are reduced to about a half of the image signal beforeapplication of mask processing, enabling disassembling to an optimumfrequency band.

In the embodiment that is described above, a pyramid algorithm was usedas the decomposing method to multi-resolution space. This invention isnot restricted to the method only. For instance, a scaling function orWavelet transformation/inverse transformation can be used. When Waveletconversion is used, emphasizing processing can be performed in anydirection (vertical direction, horizontal direction, or diagonaldirection).

A pyramid algorithm is described below. FIG. 11 is a block diagramshowing a configuration example of a decomposing section that executes apyramid algorithm. In the diagram, symbol ↑ indicatesinterpolation-processing, symbol 75 indicates down sampling, and Findicates filter processing. This sample shows processing for obtainingdifferential image signals b0 to bL-1.

In the embodiment, conversion processing that is described later isapplied to unsharp image signals or differential image signals through apyramid algorithm. The pyramid algorithm is to generate image signals ofthe resolution according to the frequency component signal bydown-sampling images and to perform processing with the image signals.Therefore, in this invention, varying in the resolution refers tovarying in the resolution of the images obtained through the pyramidalgorithm.

As shown in the diagram, when digital image signal S that indicates anoriginal image signal is input in processing method 30, the signal isfiltered by low pass filters in filtering method 20. These low passfilters roughly correspond to the two dimensional Gaussian distributionon a 5×5 grid as shown in FIG. 12. Image signal S that is filtered bylow pass filters as described above sampled at every second pixel infiltering method 20, generating low resolution approximate image signalg1.

Low resolution approximate signal g1 is ¼ of the original image signalin size. In interpolation method 21, a pixel with value 0 isinterpolated at the sampling interval of low resolution approximationimage signal g1. This interpolation is performed by inserting a row andcolumn of value 0 at every column and every row of low resolutionapproximation image signal g1. In this way, since a pixel of value 0 isinserted in every second pixel, changes of the signal value of lowresolution approximation image signal g1, which is interpolated withinterpolation by pixel of value 0, are not smooth although the image isblurred.

After interpolation is performed as described above, low resolutionimage signal g1′ is obtained by applying filtering processing again forlow resolution approximation image g1 through the low pass filters thatare shown in FIG. 12. Changes of the signal value of low resolutionapproximation image signal g1′ are smoother than low resolutionapproximation image signal g1 that was interpolated as described above.

Instead of using low pass filters after applying interpolation of 0 asdescribed above, interpolation-processing can be performed by initiallyapplying linear interpolation, spline interpolation, orinterpolation-processing by weighting according to the sampling functionand applying the same processing subsequently.

In comparison to the original image signal, the image signal appears insuch a way where the frequencies higher than the half way is eliminated.This is because the image size is reduced to ¼, interpolation is appliedwith a pixel of value 0 for every second pixel, and filtering processingis applied through the flow pass filters that are shown in FIG. 12,creating the condition where the image of the frequency bands whosespacial frequency is higher than the half way is blurred by the Gaussianfunction.

Low resolution approximation image signal g1′ is subtracted from theoriginal image signal by subtracter 22, generating differential imagesignal b0. This subtraction is performed between the original imagesignal and low resolution approximation image signal g1′ regardingmutually corresponding pixels. Here, for low resolution approximationsignal g1′, the image of frequency bands higher than the half way of thespace frequencies of the original image signal is blurred, differentialimage signal b0 is an image signal that indicates only the frequencybands higher than the half way among the original image signals. Thatis, differential image signal b0 indicates the image signal of frequencybands N/2 to N among Nyquist frequency N of the original image signal asshown in FIG. 13.

Low resolution approximation image signal g1 is input to filteringmethod 20 and is processed by filtering processing by low pass filtersthat are shown in FIG. 12. After being processed by filteringprocessing, low resolution approximation image signal g1 is sampled atevery second pixel in filtering method 20 and as a result, lowresolution approximation image g2 is obtained. Low resolutionapproximation image signal g2 is ¼ of low resolution approximation imagesignal g1 in size, that is, 1/16 of the original image signal.

In interpolation method 21, a pixel with value 0 is interpolated at thesampling interval of low resolution approximation signal g2. Thisinterpolation is performed by inserting a row and a column of value 0 inevery column and row of low resolution approximation signal g2. In thisway, since low resolution approximation image signal g2 with pixels ofvalue 0 interpolated is blurred, changes of the signal value are notsmooth since a pixel of value 0 is inserted at every second pixel.

As shown in FIG. 12, after application of interpolation, low resolutionapproximation image signal g2′ is obtained by applying filteringprocessing again to low resolution approximation image signal g2.Changes of the signal value of low resolution approximation image signalg2′ are smoother than the those of low resolution approximation imagesignal g2 that was interpolated. In comparison to low resolutionapproximation image signal g1, the image signal of frequency bandshigher than the half way appears to have been eliminated.

In subtracter 22, low resolution approximation image signal g2′ issubtracted from low resolution approximation image signal g1 and as aresult differential image signal b1 is obtained. This subtraction isperformed between low resolution approximation signal g1 and lowresolution approximation image filter g2′ for mutually correspondingpixels. As described above, for low resolution approximation imagesignal g2′, since the image of the frequency bands higher than the halfway of the space frequencies of low resolution approximation imagesignal g1 is blurred, differential image signal b1 represents only thefrequency higher than the half way among the frequency bands of lowresolution approximation image signal g1.

That is, as shown in FIG. 13, differential image signal b1 indicatesonly the frequency hands higher than the half way of the frequency bandsof low resolution approximation image signal g1, that is an image signalof frequency bands from N/4 to N/2 among the Nyquist frequency N of theoriginal image signal. In this way, differential image signals areobtained by applying filtering processing through low pass filters ofGaussian distribution. Since the image signal with filtering processingapplied is subtracted from a low resolution approximation image signal,practically the same result as for applying filtering processing throughLaplacian filters is achieved.

As shown in FIG. 13, by repeatedly processing low resolutionapproximation image signal gk (k=0 to L-1) that was generated byfiltering and sampling in method 20 the result obtained by theprocessing described above, n number of differential signal bk (k=0 toL-1) and residual signal gL of the low resolution approximation imagesignal are obtained. The resolution of differential image signal bkdeteriorates gradually starting from b0. That is, the frequency bands ofthe image signal become lower and differential image signal bk indicatesfrequency bands of N/2^(k+1) to N/2^(k) for Nyquist frequency N of theoriginal image signal and the image size becomes ½^(2k) of the size ofthe original image.

That is, the size of differential image signal b0 with the highestresolution, is the same as the original image signal and the size ofdifferential image signal b1 with the second highest resolutionfollowing differential image signal b0 is ¼ of the size of the originalimage signal. In this way, since sizes of differential image signalsbecome smaller starting form the same size as the size of the originalimage signal and differential image signals are practically the sameimage signals that are generated by applying Laplacian filters, multipleresolution conversion in this embodiment is also referred to as aLaplacian pyramid algorithm.

Residue image signal gL can be assumed to be an approximation imagesignal, which is an original image signal with extremely low resolutionand in an extreme case, residue image signal gL (equivalent to a lowestfrequency image signal, which is the result generated by executing apyramid algorithm for the original image signal and applying the finalfiler processing of multiple times of filter processing performed)consists of only one image signal that represents an average value ofthe original image signal. Differential image signal bk that is obtainedin this way is stored in the memory that is not shown in the diagram.The image signal conversion processing in this invention as describedabove is performed for g1′, g2′, g3′ . . . , which are the output ofinterpolation method 21 that is shown in FIG. 11. Alternatively, theimage signal conversion processing is performed for b0, b1, b2, . . . .These unsharp image signals, g1′, g2′, g3′ . . . , are nusharp imagesignals of multiple frequency bands of different frequencycharacteristics. This invention performs the image processing usingthese unsharp image signals as described above.

The following method may also be used instead of using 0 interpolationand low pass filters. Initially, linear interpolation, splineinterpolation, or interpolation-processing by weighting according to thecardinal sign (sampling function) is performed for columns and the sameprocessing is performed for rows.

Inverse transformation is performed for differential image signal bkthat is processed by image conversion processing by this invention anddifferential image signals of other frequency bands. An example ofinputting signals b0 to bL-1 is shown here.

This inverse transformation is performed in reconstruction processingmethod 40. FIG. 14 is a block diagram that shows an example of theconfiguration of the restoration section that executes a pyramidalgorithm. Initially, image signal bL-1 is converted to image signalbL-1′, which is 4 times of the size of the original size by applyinginterpolation between each pixel in interpolation method 24. Then,addition image signal (bL-1′30 bL-2) by performing addition ofcorresponding pixels between interpolated image signal bL-1′ anddifferential image signal bL-2.

Addition image signal (bL-1′+bL-2) is input to interpolation method 24and interpolation is applied to each pixel in interpolation method 24,generating image signal bL-2′, which is 4 times the size of the originalsize. For image signal bL-2′, by adder 25, addition processing isperformed for mutually corresponding pixels with differential imagesignal bL-3, which has resolution one method higher than that ofdifferential image signal bL-2, interpolation is applied to an intervalof each pixel of added signal (bL-2′+bL-3′), generating image signalbL-3′, which is 4 times the size of that of differential image signalbL-3.

Same processing is repeated subsequently. By applying this processing todifferential image signals of higher frequency bands sequentially,finally the processing image signal Sout is obtained (frequencyemphasizing processing) by multiplying the result of addition ofinterpolation image signal b1′ and differential image signal b0 of thehighest resolution through adder 25 by β with multiplier 26, adding theresult to original image signal S with adder 29. Alternatively,processed image Sout is obtained (dynamic range compression processing)by adding a density compensation to the result of subtraction of b0′from original image signal S and adding the result to original imagesignal S by adder 29.

Processed image signal S′ that was obtained in this way is input to animage signal output method and is displayed as a visual image. Thisimage signal output method may be a display method such as CRT. Themethod may also be a storage apparatus that performs optical scanrecording on a sensitized film.

In the embodiment, a pyramid algorithm was used as the disassemblymethod to multiple resolution space, however, other algorithms may alsobe used. For instance, a scaling function or Wavelet conversion/inversetransformation may be used. When Wavelet conversion is used, emphasizingprocessing can be performed for any direction (vertical direction,horizontal direction, or diagonal direction).

In the embodiment that was described above, the conversion functionvaries according to the density of the unsharp image signal to beconverted. The conversion function may be an unsharp image signal or anoriginal image signal in the lowest frequency band.

This invention is related to configuration of filtering method 20 orinterpolation method 21 that executes a pyramid algorithm as descriedabove. This invention may also use simple average filters (binomialfilters) of 2×2. When a simple average filter is applied to an imagesignal repeatedly, the weight of the filter becomes close to that ofGaussian distribution. By using this factor, unsharp image signals canbe generated in frequency processing at a high speed and easily. Bychanging the number of times filter processing is performed, frequencycharacteristics can be adjusted easily.

By manipulating filter frequency characteristics, more delicatefrequency manipulations of processing images are enabled.

The concept of filtering processing in this invention is describedbelow. FIG. 15 illustrates filtering processing. Here, filteringprocessing by one-dimensional filters is explained. Initially, weightingfilters that are shown at the bottom of the diagram are discussed. Asshow in the diagram, filter coefficients are ¼, ½, and ¼. Application ofthese filters to pixels a, b, and c, will be represented as(¼)×a+(½)×b+(¼)×c and the operation result will be (a+2b+c)/4.

Application of simple filters of weighting coefficients ½ and ½ topixels a, b, and c is represented as (½)×(a+b) and (½)×(b+c)respectively in the first operation. Application of simple average ½ and½ these values is represented as (a+2b+c)/4. This result matches thevalue produced at the first operation performed using weighting filters¼, ½, and ¼. The above result indicates that repeated operation ofsimple average filters fluctuate weighting, producing anon-simple-average weighting value.

FIG. 16 shows a simple average filter of 2×2. As shown in the diagram,the weight of each value is ¼, indicating that the filter is a simpleaverage. After the simple average filter is repeated multiple times, theweight becomes no longer a simple average. FIG. 17 shows therelationship (condition in which normalization is not performed) betweendistribution of 8 simple average filter repetitions and Gaussiandistribution. The upper row contains filter weights after 8 repetitionsof simple average and the lower row indicates Gaussian distribution whenvariance value=2. The comparison between the values in the upper rowsand the values in the lower row indicates that the weight coefficientsindicate the values close to Gaussian distribution.

In this invention, by using repeatedly a simple average filter that isshown in FIG. 16, high-speed filtering processing can be performed incomparison to the case where conventional weighting filters are used andprocessing can be performed at a high speed also for the algorithm thatrequires filtering processing in multiple times such as a pyramidalgorithm. When a mask needs to be changed to change frequencycharacteristics in the conventional method, in this invention, frequencycharacteristics can be changed simply by changing the number of timesfiltering is performed for a simple average filter. That is, byspecifying a simple average repetition count, frequency characteristicscan be manipulated.

Assuming that the mask used is a simple average of 2×2 as shown in FIG.16, unsharp image signals can be generated by applying this filterrepeatedly. This mask has a feature of becoming rapidly close to aGaussian mask as the application increases. For instance, twoapplications of this mask is equivalent to application of the mask withthe weighting as shown in FIG. 18.

As the mask application count increases, averaging increases,consequently, generating frequency characteristics of the unsharp imagesignal with the high frequency bands truncated. FIG. 19 illustrates therelationship between the number of filtering stages and the response.The vertical axis represents frequency f and the horizontal axisrepresents response. When the number of filtering stages is low, thecharacteristics represented by f1 are shown. When the number offiltering stages is high, the characteristics represented by f2 areshown. When the number of filtering stages is high, high frequencycomponent signals are truncated, lowering the frequency characteristics.

In a pyramid algorithm, interpolation-processing is performed toreconstruct an image size. In this case, the closer the shape of thefilter used for interpolation-processing to a cardinal sign function(sampling function or sinc function), the closer the frequencycharacteristics of the interpolated image signal becomes to thefrequency characteristics of the pre-interpolation image signal. Forinstance, if the interpolation-processing is simple interpolation, thepre-interpolation image signal has frequency characteristics with highfrequencies truncated. By combining these types of processing, delicateadjustments are enabled for frequency characteristics of differentialimage signals.

By changing a down sampling rate of an image signal by changingfrequency characteristics of an unsharp image signal through a maskchange, and changing the number of frequency segments through an imagesignal, adjustments such as reduction of the number of segmentsaccording to the image signal are enabled, simplifying processing.

By adjusting the contents of each mask processing such as number offiltering stages, the processing time can be easily reduced and aprocessing algorithm can be structured easily considering both thepicture quality and the processing time.

According to the present invention, the following effects could beattained.

-   (1) Since said high-frequency component signal is obtained that by    adding a differential image signal obtained by applying conversion    processing to unsharp image signals of multiple frequency bands that    are generated from said original image signal and adding a    difference between said unsharp image signal and said converted    image signal, a differential image signal that is added to an    original image signal is adjusted by converting an unsharp image    signal and, consequently, a processing image signal can be created    with controlling noise and artifacts together with an edge emphasis.-   (2) Since said differential image signals derive from either    differences between said unsharp image signals in adjacent pairs of    frequency bands or differences between said original image signal    and said converted unsharp image signals, frequency band overlapping    sections of differential image signals are reduced by taking    differences between adjacent pairs of unsharp image signals and by    applying conversion processing to said unsharp image signals,    operation in band units is enabled.-   (3) Since said conversion processing applied to said unsharp image    signals of said multiple frequency bands converts pixel values of    said original image signals of said unsharp image signals based on    non-linear conversion, edge emphasis and control of noise and    artifacts are enabled by performing non-linear conversion.-   (4) Since conversion processing that is applied to unsharp image    signals of said multiple frequency bands is determined by said    original image signal or said unsharp image signals of said multiple    frequency band, processing depending on an unsharp tendency of image    signals can be performed, and consequently, effective edge emphasis    and noise and artifacts control are enabled.-   (5) Since said conversion processing applied to said unsharp image    signals of said multiple frequency bands is determined by said    unsharp image signals in adjacent pairs of frequency bands or said    original image signal, processing depending on an unsharp tendency    of image signals can be performed, and consequently, effective edge    emphasis and noise and artifacts control are enabled.-   (6) Since said conversion processing that is applied to said unsharp    image signals of said multiple frequency bands varies according to a    pixel value of either one of said unsharp image signals or said    original unsharp image signal that is used for generating said    differential image signals, processing depending on pixels prior to    unsharp processing can be performed, enabling conversion with more    consideration to high frequency component signals, and consequently,    more effective edge emphasis and suppress of artifacts and noise are    enabled.-   (7) Since said conversion processing that is applied to said unsharp    image signals of said multiple frequency bands vary according to    said unsharp image signals, adjustments depending on frequency bands    can be made and more effective edge emphasis and suppress of noise    and artifacts are enabled.-   (8) Since said conversion processing that is applied to said unsharp    image signals suppress averaging of image signals, unsharpness is    suppressed in a high contrast area, which is a cause of    overshoot/undershoot and consequently, effective edge emphasis and    control of noise and artifacts are enabled.-   (9) Since said conversion processing that is applied varies    depending on pixel values of said unsharp image signals to be    processed by said conversion processing, processing depending on    signal values of unsharp image signals become possible and by    enhancing control of artifacts of signals with noticeable noise    (density), more effective edge emphasis and control of noise and    artifacts are enabled.-   (10) Since said conversion processing that is applied to said    unsharp image signals varies depending on pixel values of said    unsharp images at lowest frequency band, changes of conversion of    unsharp image signals may follow a major configuration of an    original image signal.-   (11) Since said conversion processing that is applied to said    unsharp image signals varies depending on pixel values of said    original signal, changes of conversion of unsharp images may follow    an original image signal faithfully.-   (12) Since a degree of averaging control of said conversion    processing that is applied to said unsharp image signals increases    as frequency bands of said unsharp images become lower, a degree of    averaging suppression increases as frequency bands become lower and    consequently, image signals of higher quality may be obtained.-   (13) Since a degree of averaging control of said conversion    processing that is applied to said unsharp image signals increases    as frequency bands of said unsharp image signals become higher,    averaging suppression increases for high frequency component signals    that tend to contain many noise component signals and consequently    effective edge emphasis and control of noise and artifacts are    enabled.-   (14) Since compensation signals are obtained by applying conversion    processing to unsharp image signals of multiple frequency bands that    are generated from said original signal, generating high-frequency    image signals that are obtained adding differential image signals    that are obtained by differences between said unsharp image signals    and said image signals generated after said conversion processing,    and obtaining differences of said low frequency image signals from    results of conversion of low frequency image signals that are    obtained from differences between said high frequency image signals    and said original image signal, a compensation section that is added    to an original image signal or super low frequency image signal,    processing image signals can be generated by applying both image    signal dynamic range compression and control of noise and artifacts.-   (15) Since said conversion processing varies depending on pixel    values of said unsharp image signals, emphasis of bands containing    many noises in signal areas where noises are noticeable can be    controlled by adjusting differential image signals that are added to    an original image signal or lowest frequency band image signals,    depending on signal values of unsharp images and consequently, more    effective edge emphasis and control of noise and artifacts are    enabled.-   (16) Since said differential image signal indicate a difference    between unsharp image signals of a pair of adjacent frequency bands    or a difference between an original image signal and a converted    unsharp image signal, a frequency band overlapping section of each    differential signal is reduced by determining a difference between a    pair of adjacent image signals and operation by band units is    enabled by employing conversion processing to unsharp image signals.-   (17) Since said unsharp image signal on which said conversion    processing depends is said image signals used when said differential    signals were obtained, an unsharp image signal of an image size    identical to a converted image signal can be used when a pyramid    algorithm is used also and consequently, processing can be    simplified.-   (18) Since conversion processing that is applied to said multiple    differential image signals varies depending on said differential    image signals, adjustments depending on frequency bands are enabled    and consequently more effective edge emphasis and control of noise    and artifacts are enabled.-   (19) Since conversion processing that is applied to said    differential image signals absolute values of pixel values in at    least some image signals, emphasis on a high contrast area, which is    a cause of overshoot/undershoot is suppressed and consequently, more    effective edge emphasis and control of noise and artifacts are    enabled.-   (20) Since control of absolute values of image signals by conversion    processing that is applied to said differential image signals    increases as frequency bands of said differential image signals    become lower, the lower the frequency band in the differential image    signal, the greater the control of the absolute value becomes,    enabling generation of sharper image signals with control of noise    and artifacts more effectively.-   (21) Since control of absolute values of image signals by conversion    processing that is applied to said differential image signals    increase as frequency bands of said differential image signals    become higher, suppression over absolute values increases as a    frequency band that tends to contain many noise component signals    becomes high and consequently, more effective edge emphasis and    suppression of noise and artifacts are enabled.-   (22) Since a conversion function that actualizes given frequency    characteristics is determined by specifying frequency    characteristics and processing is performed by said conversion    function that was determined, users only need to specify required    frequency characteristics without having to be aware of various    parameters to be set and consequently, processing is simplified.-   (23) Since specification of said frequency characteristics can be    changed according to a density, users can easily specify processing    depending on signal values such as suppression of noise emphasis by    operating frequency characteristics of signal areas containing    noticeable noise.-   (24) Since specification of said frequency characteristics can be    changed depending on a density for each of unsharp image signals or    differential image signals, users can easily set an intensity of    processing depending on signal values for each frequency band.-   (25) Since a set of parameters is specified in said frequency    characteristic processing and processing can be specified by    selecting said set of parameters, users can select an optimum    parameter set easily without manipulating many parameters.-   (26) Since frequency characteristics of processing image signals are    changed by changing mask frequencies used for mask processing for    generating said unsharp image signals, more delicate frequency    characteristic adjustments are enabled for processing images by    changing mask frequency characteristics.-   (27) Since said mask processing is specific filter repetition    processing, frequency characteristics can be adjusted at high speed    without using multiple filters.-   (28) Since a mask of said repetition processing is a simple average,    frequency characteristics can be adjusted at high speed.-   (29) Since a mask of said repetition processing is a simple average    of 2 pixels×2 pixels, unsharp image signals can be generated at high    speed and also according to normal distribution.-   (30) Since frequency characteristics of said processing image    signals are specified by a processing repetition count of said    repetition processing, frequency characteristics can be specified    easily.-   (31) Since frequency characteristics of said processing image    signals are specified by designating a weight of a mask at    generation of unsharp image signals using a variance value of normal    distribution and processing is performed by calculating said mask    processing repetition count approximating with normal distribution    of said variance value that was specified, frequency characteristics    can be specified easily.-   (32) Since said mask processing varies depending on said unsharp    image signal, frequency characteristics may be adjusted according to    a frequency band.-   (33) Since said mask processing varies depending on an original    image signal, frequency characteristics may be adjusted according to    a type of an original image signal, for instance, body parts    examined.-   (34) Since said masking processing varies depending on frequency    characteristics of said original image signal, adjustments according    to frequency characteristics of an original image signal are enabled    for suppressing frequency bands with excessive noise.-   (35) Since frequency characteristics of image processing signals are    changed by a changing interpolate-processing method for addition or    subtraction of said image signals of different resolutions, more    delicate frequency characteristic adjustments of processing image    signals are enabled by changing frequency of    interpolation-processing.-   (36) Since said interpolation-processing is performed based on a    sampling function of an original image signal, frequency    characteristics of unsharp image signals can be reproduced more    faithfully.-   (37) Since said interpolation-processing performs linear    interpolation, processing can be preformed at a high speed without    making major changes in frequency characteristics of unsharp image    signals.-   (38) Since said interpolation-processing is spline interpolation,    smooth interpolation is achieved.-   (39) Since said interpolation-processing varies depending on    frequency bands of interpolation image signals, frequency    characteristics may be adjusted for each frequency band.-   (40) Since said interpolation-processing varies depending on an    original image signal, frequency characteristics may be adjusted    according to a type of an original image signal, for instance body    parts examined.-   (41) Since said interpolation-processing varies according to    frequency characteristics of an original image signal, adjustments    may be made according to frequency characteristics of an original    image signal such as suppression of frequency bands with many noise    signals.-   (42) Since unsharp image signals are generated by mask processing    through said pyramid algorithm and a reduction rate of unsharp image    signals by down sampling changes according to frequency    characteristics, a processing speed may be increased efficiently by    changing a reduction rate of image signals through frequency    characteristics of a mask.-   (43) Since said mask processing varies depending on unsharp image    signals, frequency characteristics may be adjusted according to a    frequency band.-   (44) Since said mask processing varies depending on an original    image signal, frequency characteristics may be adjusted according to    a type of an original image, for instance, body parts examined.-   (45) Since said mask processing varies depending on frequency    characteristics of an original image signal, adjustments may be made    according to frequency characteristics of an original image signal    such as controlling of frequency bands with many noise signals.-   (46) Since changes of said frequency characteristics of a mask or    changes of interpolation-processing are determined by specified    frequency characteristics, users may easily generate image signals    of required frequency characteristics by determining characteristics    of filters from said frequency characteristics.-   (47) Since specification of said frequency characteristics may be    changed according to a density of an original image signal or an    unsharp image signal, frequency characteristics may be adjusted    effectively such as suppression of emphasis on signal areas where    noise is noticeable.-   (48) Since specification of said frequency characteristics may be    changed according to a density of an original image signal or an    unsharp image signal for each of said unsharp image signals or a    differential image signal, frequency characteristics may be adjusted    efficiently such as controlling of emphasis of signal areas where    noise is noticeable in a frequency area containing many noise    signals.-   (49) Since the image processing apparatus retains a set of    parameters required for processing said frequency characteristics,    wherein processing is specified by selecting said set of parameters,    users may achieve optimum processing by specifying a set of    parameters without setting detailed parameters.-   (50) Since filtering processing for generating said unsharp image    signals is repetition of specific filters, processing may be    simplified.-   (51) Since a mask of said repetition processing is a simple average,    processing may be simplified and a processing speed may be    increased.-   (52) Since a mask of said repetition processing is a simple average    of 2 pixels×2 pixels, effects equivalent to those achieved from    processing by a weighting mask according to Gaussian distribution    may be obtained.-   (53) Since said mask processing varies depending on an unsharp    image, frequency characteristics may be adjusted according to a    frequency band.-   (54) Since said mask processing varies depending on an original    image signal, frequency characteristics may be adjusted according to    a type of an original image, for instance, body parts examined.-   (55) Since said mask processing varies depending on frequency    characteristics of said original image signal, processing may be    varied according to frequency characteristics of an original image    signal such as suppression of frequency bands containing many noise    signals.-   (56) Since a repetition count of said single average of 2×2 is 16 or    greater, frequency band areas contained in each unsharp image signal    are reduced to about a half of frequency bands before application of    mask processing, enabling disassembly to an optimum frequency band.-   (57) Since a repetition count of said simple average of 2×2 is 8 or    greater, frequency bands contained in each unsharp image signal are    reduced to about a half of frequency band before application of mask    processing, enabling disassembly to an optimum frequency band.

Disclosed embodiment can be varied by a skilled person without departingfrom the spirit and scope of the invention.

1. An image-processing apparatus, comprising: an unsharp image-signalgenerating section to generate unsharp image-signals from an originalimage-signal in respect to a plurality of frequency-bands; adifferential processing section to generate differential image-signalsfrom differences between the original image-signal and the unsharpimage-signals, and to apply a conversion-processing to the differentialimage-signals so as to generate converted differential image-signals;and an addition processing section to add said converted differentialimage-signals to the original image-signal or a lowest frequencyimage-signal to generate a processed image-signal; wherein theconversion-processing varies depending on pixel values of the unsharpimage-signals so as to suppress a conversion coefficient of thedifferential image-signals to the converted differential image-signalsin sections where the pixel value of the unsharp image-signal representslower density.
 2. The image-processing apparatus of claim 1, furthercomprising: a compensation-signal calculating section to generate acompensation-signal which is derived from a low-frequency componentsignal obtained by subtracting a total sum of said converteddifferential image-signals from said original image-signal; wherein saidaddition processing section adds said compensation-signal, instead ofsaid converted differential image-signals, to said original image-signalor said lowest frequency image-signal to generate said processedimage-signal.
 3. The image-processing apparatus of claim 2, wherein saiddifferential image-signals on which said conversion-processing dependsare either anyone of image-signals utilized for obtaining saiddifferential image-signals or both of them.
 4. The image-processingapparatus of claim 2, wherein said conversion-processing applied to saiddifferential image-signals varies depending on said differentialimage-signals.
 5. The image-processing apparatus of claim 2, whereinsaid conversion-processing applied to said differential image-signals isa suppression-processing for suppressing an absolute pixel value atleast at a part of image-signals.
 6. The image-processing apparatus ofclaim 5, wherein the lower a frequency-band in which said differentialimage-signals reside is, the stronger a power of suppressing saidabsolute pixel value of said image-signals in saidsuppression-processing is.
 7. The image-processing apparatus of claim 5,wherein the higher a frequency-band in which said differentialimage-signals reside is, the stronger a power of suppressing saidabsolute pixel value of said image-signals in saidsuppression-processing is.
 8. The image-processing apparatus of claim 2,wherein a conversion-function is determined by designating a frequencycharacteristic, so as to realize a given frequency characteristic, andprocessing are conducted on the basis of said conversion-function. 9.The image-processing apparatus of claim 8, wherein said frequencycharacteristic can be changed depending on density.
 10. Theimage-processing apparatus of claim 8, wherein said frequencycharacteristic can be changed depending on density of either saidoriginal image-signal or said unsharp image-signals for everydifferential image-signal.
 11. The image-processing apparatus of claim8, wherein sets of parameters for processing said frequencycharacteristic are provided in said image-processing apparatus, a kindof processing can be designated by selecting one set out of said sets ofparameters.
 12. An image-processing apparatus, comprising: afilter-processing section to apply a Gaussian weighting mask-processingto one pixel of an original image-signal, representing a plurality ofpixels, with a mask so as to generate filtered original image-signals;an unsharp image-signal generating section to generate unsharpimage-signals from said filtered original image-signals; a differentialprocessing section to generate differential image-signals fromdifferences between the original image-signal and the unsharpimage-signals, or from differences between the unsharp image-signalsthemselves; and an addition processing section to add the differentialimage-signals to the original image-signal or a lowest frequencyimage-signal with respect to the original image-signal in order togenerate a processed image-signal; wherein a frequency characteristic ofthe processed image-signal can be varied by changing a frequencycharacteristic of the mask employed for the mask-processing, and whereinthe mask-processing is repetitions of filter-processing with a simpleaverage filter.
 13. An image-processing apparatus, comprising: anunsharp image-signal generating section applying a Gaussian weightingmask-processing to one pixel and generating a plurality of unsharpimage-signals from a original image-signal, representing a plurality ofpixels; a differential processing section to generate differentialimage-signals from the unsharp image-signals or the originalimage-signal; and an addition processing section to add the differentialimage-signals to the original image-signal or a lowest frequencyimage-signal with respect to the original image-signal in order togenerate a processed image-signal; wherein repetitions offilter-processing with a simple average filter are conducted forgenerating each of the unsharp image-signals.
 14. The image-processingapparatus of claim 13, further comprising: a compensation-signalcalculating section to generate a compensation-signal which is derivedfrom a low-frequency component signal obtained by subtracting a totalsum of said differential image-signals from said original image-signal;wherein said addition processing section adds said compensation-signal,instead of said differential image-signals, to said originalimage-signal or said lowest frequency image-signal to generate saidprocessed image-signal.
 15. The image-processing apparatus of claim 13,wherein a mask employed for said repetitions of filter-processing is asimple average of 2 pixels×2 pixels.
 16. The image-processing apparatusof claim 13, wherein a mask-processing varies depending on said unsharpimage-signals.
 17. The image-processing apparatus of claim 13, wherein amask-processing varies depending on said original image-signal.
 18. Theimage-processing apparatus of claim 13, wherein a mask-processing variesdepending on a frequency characteristic of said original image-signal.19. The image-processing apparatus of claim 15, wherein a number ofrepetitions of said single average of 2 pixels×2 pixels is not less than16.
 20. The image-processing apparatus of claim 15, wherein a number ofrepetitions of said single average of 2 pixels×2 pixels not less than 8.